Generating ratings predictions using neuro-response data

ABSTRACT

An example system disclosed herein for transforming neuro-response data into media ratings includes a data collector to obtain first neuro-response from a first subject exposed to a first media and second neuro-response data from a second subject exposed to a second media. The first media broadcast is before a time of the second media. The example system includes an analyzer to integrate the first neuro-response data with ratings data for the first media to generate a first rating for the first media. The ratings data is based on set-top box data associated with a media presentation device presenting the first media. The analyzer is to transform the second neuro-response data into a second rating for the second media based on the first rating.

FIELD OF THE DISCLOSURE

The present disclosure relates to using neuro-response data to generate ratings predictions.

BACKGROUND

Conventional systems for performing ratings predictions of media materials such as programming and advertising are limited. Some ratings predictions can be made based on survey and focus group based feedback. However, conventional systems for predicting ratings are subject to syntactic, metaphorical, cultural, and interpretive errors that prevent accurate and repeatable predictions.

Consequently, it is desirable to provide improved methods and apparatus for generating ratings predictions by using neuro-response data such as central nervous system, autonomic nervous system, and effector system measurements.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure may best be understood by reference to the following description taken in conjunction with the accompanying drawings, which illustrate particular examples.

FIG. 1 illustrates one example of a system for performing ratings prediction using neuro-response data.

FIG. 2 illustrates examples of media attributes that can be included in a repository.

FIG. 3 illustrates examples of data models that can be used with a ratings prediction generator.

FIG. 4 illustrates one example of a query that can be used with the ratings prediction generator

FIG. 5 illustrates one example of a report generated using a ratings prediction generator.

FIG. 6 illustrates one example of a technique for performing ratings prediction.

FIG. 7 illustrates one example of technique for performing ratings prediction.

FIG. 8 provides one example of a system that can be used to implement one or more mechanisms.

DETAILED DESCRIPTION

Reference will now be made in detail to some specific examples of the disclosure including the best modes contemplated by the inventors for carrying out the disclosure. Examples of these specific examples are illustrated in the accompanying drawings. While the disclosure is described in conjunction with these specific examples, it will be understood that it is not intended to limit the disclosure to the described examples. On the contrary, it is intended to cover alternatives, modifications, and equivalents as may be included within the spirit and scope of the disclosure n as defined by the appended claims.

For example, the techniques and mechanisms of the present disclosure will be described in the context of particular types of data such as central nervous system, autonomic nervous system, and effector data. However, it should be noted that the techniques and mechanisms of the present disclosure apply to a variety of different types of data. It should be noted that various mechanisms and techniques can be applied to any type of stimuli. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. Particular examples of the present disclosure may be implemented without some or all of these specific details. In other instances, well known process operations have not been described in detail in order not to unnecessarily obscure the present disclosure.

Various techniques and mechanisms of the present disclosure will sometimes be described in singular form for clarity. However, it should be noted that some examples include multiple iterations of a technique or multiple instantiations of a mechanism unless noted otherwise. For example, a system uses a processor in a variety of contexts. However, it will be appreciated that a system can use multiple processors while remaining within the scope of the present disclosure unless otherwise noted. Furthermore, the techniques and mechanisms of the present disclosure will sometimes describe a connection between two entities. It should be noted that a connection between two entities does not necessarily mean a direct, unimpeded connection, as a variety of other entities may reside between the two entities. For example, a processor may be connected to memory, but it will be appreciated that a variety of bridges and controllers may reside between the processor and memory. Consequently, a connection does not necessarily mean a direct, unimpeded connection unless otherwise noted.

Overview

A system evaluates media materials such as programs and advertising by obtaining neuro-response data such as central nervous system, autonomic nervous system, and effector system measurements from subjects exposed to the media materials. Media materials are categorized and/or tagged. Corresponding media materials eliciting similar neuro-response data from users in similar demographic categories are identified. In some examples, ratings predictions are generated by identifying ratings associated with corresponding media materials eliciting similar neuro-response data.

EXAMPLES

Ratings prediction is generally performed by using survey and focus group based feedback from subjects exposed to media materials. Media materials may include programs, advertising, movies, shows, games, etc. However, conventional ratings prediction mechanisms often inaccurately predict ratings. Conventional systems do not use neuro-behavioral and neuro-physiological response blended manifestations in assessing the user responses and do not elicit predicted responses to media materials.

In these respects, a ratings prediction generator device using central nervous system, autonomic nervous system and effector system measurements according to the present disclosure substantially departs from the conventional concepts and designs and provides a mechanism for the ratings prediction for different types of media materials across a variety of demographic groups and subgroups.

According to various examples, techniques and mechanisms are provided that can not only predict ratings, but can also measure characteristics such as attention, priming, retention, and emotional response characteristics for media materials.

According to various examples, the techniques and mechanisms of the present disclosure may use a variety of mechanisms such as survey based responses, statistical data, and/or neuro-response measurements such as central nervous system, autonomic nervous system, and effector measurements to improve ratings prediction. Some examples of central nervous system measurement mechanisms include Functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), Magnetoencephalography (MEG), and Optical Imaging. Optical imaging can be used to measure the absorption or scattering of light related to concentration of chemicals in the brain or neurons associated with neuronal firing. MEG measures magnetic fields produced by electrical activity in the brain. fMRI measures blood oxygenation in the brain that correlates with increased neural activity. However, current implementations of fMRI have poor temporal resolution of few seconds. EEG measures electrical activity associated with post synaptic currents occurring in the milliseconds range. Subcranial EEG can measure electrical activity with the most accuracy, as the bone and dermal layers weaken transmission of a wide range of frequencies. Nonetheless, surface EEG provides a wealth of electrophysiological information if analyzed properly. Even portable EEG with dry electrodes provides a large amount of neuro-response information.

Autonomic nervous system measurement mechanisms include Electrocardiograms (EKG), pupillary dilation, etc. Effector measurement mechanisms include Electrooculography (EOG), eye tracking, facial emotion encoding, reaction time etc.

According to various examples, the techniques and mechanisms of the present disclosure intelligently blend multiple modes and manifestations of precognitive neural signatures with cognitive neural signatures and post cognitive neurophysiological manifestations to more accurately perform ratings prediction. In some examples, autonomic nervous system measures are themselves used to validate central nervous system measures. Effector and behavior responses are blended and combined with other measures. According to various examples, central nervous system, autonomic nervous system, and effector system measurements are aggregated into a measurement that allows ratings prediction.

In particular examples, subjects are exposed to media material and data such as central nervous system, autonomic nervous system, and effector data is collected during exposure. In particular examples, specific event related potential (ERP) analyses and/or event related power spectral perturbations (ERPSPs) are evaluated for different regions of the brain both before a subject is exposed to media and each time after the subject is exposed to media.

According to various examples, pre-stimulus and post-stimulus differential as well as target and distracter differential measurements of ERP time domain components at multiple regions of the brain are determined (DERP). Event related time-frequency analysis of the differential response to assess the attention, emotion and memory retention (DERPSPs) across multiple frequency bands including but not limited to theta, alpha, beta, gamma and high gamma is performed. In particular examples, single trial and/or averaged DERP and/or DERPSPs can be used to enhance the resonance measure and determine priming levels for various products and services.

A variety of media materials such as entertainment and marketing materials, media streams, billboards, print advertisements, text streams, music, performances, sensory experiences, etc. can be analyzed. Media materials may involve audio, visual, tactile, olfactory, taste, etc. According to various examples, enhanced neuro-response data is generated using a data analyzer that performs both intra-modality measurement enhancements and cross-modality measurement enhancements. According to various examples, brain activity is measured not just to determine the regions of activity, but to determine interactions and types of interactions between various regions. The techniques and mechanisms of the present disclosure recognize that interactions between neural regions support orchestrated and organized behavior. Attention, emotion, memory, and other abilities are not merely based on one part of the brain but instead rely on network interactions between brain regions.

The techniques and mechanisms of the present disclosure further recognize that different frequency bands used for multi-regional communication can be indicative of the effectiveness of stimuli. In particular examples, evaluations are calibrated to each subject and synchronized across subjects. In particular examples, templates are created for subjects to create a baseline for measuring pre and post stimulus differentials. According to various examples, stimulus generators are intelligent and adaptively modify specific parameters such as exposure length and duration for each subject being analyzed.

A variety of modalities can be used including EEG, GSR, EKG, pupillary dilation, EOG, eye tracking, facial emotion encoding, reaction time, etc. Individual modalities such as EEG are enhanced by intelligently recognizing neural region communication pathways. Cross modality analysis is enhanced using a synthesis and analytical blending of central nervous system, autonomic nervous system, and effector signatures. Synthesis and analysis by mechanisms such as time and phase shifting, correlating, and validating intra-modal determinations allow generation of a composite output characterizing the significance of various data responses.

According to various examples, media material is categorized and/or tagged to allow identification of similar media material or media material portions. In particular examples, survey based and actual expressed responses and actions for particular groups of users are integrated with media materials and neuro-response data and stored in a ratings prediction generator repository. According to particular examples, ratings predictions can be made by analyzing neuro-response data. In particular examples, similarly categorized media materials with corresponding neuro-response data can be obtained from a ratings prediction generator repository to predict ratings and popularity of media material being evaluated.

FIG. 1 illustrates one example of a system for performing ratings prediction using central nervous system, autonomic nervous system, and/or effector measures. According to various examples, the ratings prediction system includes a media materials presentation device 101. In particular examples, the media materials presentation device 101 is merely a display, monitor, screen, etc., that provides media material to a user. The media material may be a media clip, a commercial, movie, magazine, audio presentation, game, etc., and may even involve particular tastes, smells, textures and/or sounds. The media materials may be a variety of stimulus materials and can involve a variety of senses and occur with or without human supervision. Continuous and discrete modes are supported. According to various examples, the media materials presentation device 101 also has protocol generation capability to allow intelligent customization of stimuli provided to multiple subjects in different markets.

According to various examples, media materials presentation device 101 could include devices such as televisions, cable consoles, computers and monitors, projection systems, display devices, speakers, tactile surfaces, etc., for presenting the stimuli including but not limited to advertising and entertainment from different networks, local networks, cable channels, syndicated sources, websites, internet content aggregators, portals, service providers, etc.

According to various examples, the subjects 103 are connected to data collection devices 105. The data collection devices 105 may include a variety of neuro-response measurement mechanisms including neurological and neurophysiological measurements systems such as EEG, EOG, MEG, EKG, pupillary dilation, eye tracking, facial emotion encoding, and reaction time devices, etc. According to various examples, neuro-response data includes central nervous system, autonomic nervous system, and effector data. In particular examples, the data collection devices 105 include EEG 111, EOG 113, and fMRI 115. In some instances, only a single data collection device is used. Data collection may proceed with or without human supervision.

The data collection device 105 collects neuro-response data from multiple sources. This includes a combination of devices such as central nervous system sources (EEG), autonomic nervous system sources (GSR, EKG, pupillary dilation), and effector sources (EOG, eye tracking, facial emotion encoding, reaction time). In particular examples, data collected is digitally sampled and stored for later analysis. In particular examples, the data collected could be analyzed in real-time. According to particular examples, the digital sampling rates are adaptively chosen based on the neurophysiological and neurological data being measured.

In one particular example, the ratings prediction system includes EEG 111 measurements made using scalp level electrodes, EOG 113 measurements made using shielded electrodes to track eye data, fMRI 115 measurements performed using a differential measurement system, a facial muscular measurement through shielded electrodes placed at specific locations on the face, and a facial affect graphic and video analyzer adaptively derived for each individual.

In particular examples, the data collection devices are clock synchronized with a media materials presentation device 101. In particular examples, the data collection devices 105 also include a condition evaluation subsystem that provides auto triggers, alerts and status monitoring and visualization components that continuously monitor the status of the subject, data being collected, and the data collection instruments. The condition evaluation subsystem may also present visual alerts and automatically trigger remedial actions. According to various examples, the data collection devices include mechanisms for not only monitoring subject neuro-response to media materials, but also include mechanisms for identifying and monitoring the media materials. For example, data collection devices 105 may be synchronized with a set-top box to monitor channel changes. In other examples, data collection devices 105 may be directionally synchronized to monitor when a subject is no longer paying attention to media material. In still other examples, the data collection devices 105 may receive and store media material generally being viewed by the subject, whether the media is a program, a commercial, or a movie. It should be noted that other stimulus materials can also be presented. The data collected allows analysis of neuro-response information and correlation of the information to actual media material and not mere subject distractions.

According to various examples, the ratings prediction system also includes a data cleanser device 121. In particular examples, the data cleanser device 121 filters the collected data to remove noise, artifacts, and other irrelevant data using fixed and adaptive filtering, weighted averaging, advanced component extraction (like PCA, ICA), vector and component separation methods, etc. This device cleanses the data by removing both exogenous noise (where the source is outside the physiology of the subject, e.g. a phone ringing while a subject is viewing a video) and endogenous artifacts (where the source could be neurophysiological, e.g. muscle movements, eye blinks, etc.).

The artifact removal subsystem includes mechanisms to selectively isolate and review the response data and identify epochs with time domain and/or frequency domain attributes that correspond to artifacts such as line frequency, eye blinks, and muscle movements. The artifact removal subsystem then cleanses the artifacts by either omitting these epochs, or by replacing these epoch data with an estimate based on the other clean data (for example, an EEG nearest neighbor weighted averaging approach).

According to various examples, the data cleanser device 121 is implemented using hardware, firmware, and/or software. It should be noted that although a data cleanser device 121 is shown located after a data collection device 105, the data cleanser device 121 like other components may have a location and functionality that varies based on system implementation. For example, some systems may not use any automated data cleanser device whatsoever while in other systems, data cleanser devices may be integrated into individual data collection devices.

In particular examples, a survey and interview system collects and integrates user survey and interview responses to combine with neuro-response data to more effectively select content for delivery. According to various examples, the survey and interview system obtains information about user characteristics such as age, gender, income level, location, interests, buying preferences, hobbies, etc. The survey and interview system can also be used to obtain user responses about particular pieces of media material.

According to various examples, the ratings prediction system includes a data analyzer 123 associated with the data cleanser 121. The data analyzer 123 uses a variety of mechanisms to analyze underlying data in the system to determine resonance. According to various examples, the data analyzer 123 customizes and extracts the independent neurological and neuro-physiological parameters for each individual in each modality, and blends the estimates within a modality as well as across modalities to elicit an enhanced response to the presented media material. In particular examples, the data analyzer 123 aggregates the response measures across subjects in a dataset.

According to various examples, neurological and neuro-physiological signatures are measured using time domain analyses and frequency domain analyses. Such analyses use parameters that are common across individuals as well as parameters that are unique to each individual. The analyses could also include statistical parameter extraction and fuzzy logic based attribute estimation from both the time and frequency components of the synthesized response.

In some examples, statistical parameters used in a blended effectiveness estimate include evaluations of skew, peaks, first and second moments, distribution, as well as fuzzy estimates of attention, emotional engagement and memory retention responses.

According to various examples, the data analyzer 123 may include an intra-modality response synthesizer and a cross-modality response synthesizer. In particular examples, the intra-modality response synthesizer is configured to customize and extract the independent neurological and neurophysiological parameters for each individual in each modality and blend the estimates within a modality analytically to elicit an enhanced response to the presented stimuli. In particular examples, the intra-modality response synthesizer also aggregates data from different subjects in a dataset.

According to various examples, the cross-modality response synthesizer or fusion device blends different intra-modality responses, including raw signals and signals output. The combination of signals enhances the measures of effectiveness within a modality. The cross-modality response fusion device can also aggregate data from different subjects in a dataset.

According to various examples, the data analyzer 123 also includes a composite enhanced effectiveness estimator (CEEE) that combines the enhanced responses and estimates from each modality to provide a blended estimate of the effectiveness. In particular examples, blended estimates are provided for each exposure of a subject to media materials. The blended estimates are evaluated over time to assess resonance characteristics. According to various examples, numerical values are assigned to each blended estimate. The numerical values may correspond to the intensity of neuro-response measurements, the significance of peaks, the change between peaks, etc. Higher numerical values may correspond to higher significance in neuro-response intensity. Lower numerical values may correspond to lower significance or even insignificant neuro-response activity. In other examples, multiple values are assigned to each blended estimate. In still other examples, blended estimates of neuro-response significance are graphically represented to show changes after repeated exposure.

According to various examples, a data analyzer 123 passes data to a resonance estimator that assesses and extracts resonance patterns. In particular examples, the resonance estimator determines entity positions in various media segments and matches position information with eye tracking paths while correlating saccades with neural assessments of attention, memory retention, and emotional engagement. In particular examples, the resonance estimator stores data in the priming repository system. As with a variety of the components in the system, various repositories can be co-located with the rest of the system and the user, or could be implemented in remote locations.

Data from various repositories is blended and passed to a ratings prediction engine to generate patterns, responses, and predictions 125. In some examples, the ratings prediction engine compares patterns and expressions associated with prior users to predict expressions of current users. According to various examples, patterns and expressions are correlated with survey, demographic, and preference data. In particular examples linguistic, perceptual, and/or motor responses are elicited and predicted. Response expression selection and pre-articulation prediction of expressive responses are also evaluated.

FIG. 2 illustrates examples of data models that may be user in a ratings prediction system. According to various examples, a media attributes data model 201 includes a channel 203, media type 205, time span 207, audience 209, and demographic information 211. A media purpose data model 213 may include intents 215 and objectives 217. According to various examples, media purpose data model 213 also includes spatial and temporal information 219 about entities and emerging relationships between entities.

According to various examples, another media attributes data model 221 includes creation attributes 223, ownership attributes 225, broadcast attributes 227, and statistical, demographic and/or survey based identifiers 229 for automatically integrating the neuro-physiological and neuro-behavioral response with other attributes and meta-information associated with the media.

According to various examples, a media priming data model 231 includes fields for identifying advertisement breaks 233 and scenes 235 that can be associated with various priming levels 237 and audience resonance measurements 239. In particular examples, the data model 231 provides temporal and spatial information for ads, scenes, events, locations, etc. that may be associated with priming levels and audience resonance measurements. In some examples, priming levels for a variety of products, services, offerings, etc. are correlated with temporal and spatial information in source material such as a movie, billboard, advertisement, commercial, store shelf, etc. In some examples, the data model associates with each second of a show a set of meta-tags for pre-break content indicating categories of products and services that are primed. The level of priming associated with each category of product or service at various insertions points may also be provided. Audience resonance measurements and maximal audience resonance measurements for various scenes and advertisement breaks may be maintained and correlated with sets of products, services, offerings, etc.

The priming and resonance information may be used to select media content suited for particular levels of priming and resonance.

FIG. 3 illustrates examples of data models that can be used for storage of information associated with tracking and measurement of resonance. According to various examples, a dataset data model 301 includes an experiment name 303 and/or identifier, client attributes 305, a subject pool 307, logistics information 309 such as the location, date, and time of testing, and media material 311 including media material attributes.

In particular examples, a subject attribute data model 315 includes a subject name 317 and/or identifier, contact information 321, and demographic attributes 319 that may be useful for review of neurological and neuro-physiological data. Some examples of pertinent demographic attributes include marriage status, employment status, occupation, household income, household size and composition, ethnicity, geographic location, sex, race. Other fields that may be included in data model 315 include subject preferences 323 such as shopping preferences, entertainment preferences, and financial preferences. Shopping preferences include favorite stores, shopping frequency, categories shopped, favorite brands. Entertainment preferences include network/cable/satellite access capabilities, favorite shows, favorite genres, and favorite actors. Financial preferences include favorite insurance companies, preferred investment practices, banking preferences, and favorite online financial instruments. A variety of product and service attributes and preferences may also be included. A variety of subject attributes may be included in a subject attributes data model 315 and data models may be preset or custom generated to suit particular purposes.

According to various examples, data models for neuro-feedback association 325 identify experimental protocols 327, modalities included 329 such as EEG, EOG, GSR, surveys conducted, and experiment design parameters 333 such as segments and segment attributes. Other fields may include experiment presentation scripts, segment length, segment details like media material used, inter-subject variations, intra-subject variations, instructions, presentation order, survey questions used, etc. Other data models may include a data collection data model 337. According to various examples, the data collection data model 337 includes recording attributes 339 such as station and location identifiers, the data and time of recording, and operator details. In particular examples, equipment attributes 341 include an amplifier identifier and a sensor identifier.

Modalities recorded 343 may include modality specific attributes like EEG cap layout, active channels, sampling frequency, and filters used. EOG specific attributes include the number and type of sensors used, location of sensors applied, etc. Eye tracking specific attributes include the type of tracker used, data recording frequency, data being recorded, recording format, etc. According to various examples, data storage attributes 345 include file storage conventions (format, naming convention, dating convention), storage location, archival attributes, expiry attributes, etc.

A preset query data model 349 includes a query name 351 and/or identifier, an accessed data collection 353 such as data segments involved (models, databases/cubes, tables, etc.), access security attributes 355 included who has what type of access, and refresh attributes 357 such as the expiry of the query, refresh frequency, etc. Other fields such as push-pull preferences can also be included to identify an auto push reporting driver or a user driven report retrieval system.

FIG. 4 illustrates examples of queries that can be performed to obtain data associated with ratings prediction. According to various examples, queries are defined from general or customized scripting languages and constructs, visual mechanisms, a library of preset queries, diagnostic querying including drill-down diagnostics, and eliciting what if scenarios. According to various examples, subject attributes queries 415 may be configured to obtain data from a neuro-informatics repository using a location 417 or geographic information, session information 421 such as testing times and dates, and demographic attributes 419. Demographics attributes include household income, household size and status, education level, age of kids, etc.

Other queries may retrieve media material based on shopping preferences of subject participants, countenance, physiological assessment, completion status. For example, a user may query for data associated with product categories, products shopped, shops frequented, subject eye correction status, color blindness, subject state, signal strength of measured responses, alpha frequency band ringers, muscle movement assessments, segments completed, etc. Experimental design based queries 425 may obtain data from a neuro-informatics repository based on experiment protocols 427, product category 429, surveys included 431, and media provided 433. Other fields that may be used include the number of protocol repetitions used, combination of protocols used, and usage configuration of surveys.

Client and industry based queries may obtain data based on the types of industries included in testing, specific categories tested, client companies involved, and brands being tested. Response assessment based queries 437 may include attention scores 439, emotion scores, 441, retention scores 443, and effectiveness scores 445. Such queries may obtain materials that elicited particular scores. In particular examples, prediction queries 447 may include linguistic response 449, perceptual response 451, cognition response 453, and motor response 455.

Response measure profile based queries may use mean measure thresholds, variance measures, number of peaks detected, etc. Group response queries may include group statistics like mean, variance, kurtosis, p-value, etc., group size, and outlier assessment measures. Still other queries may involve testing attributes like test location, time period, test repetition count, test station, and test operator fields. A variety of types and combinations of types of queries can be used to efficiently extract data.

FIG. 5 illustrates examples of reports that can be generated. According to various examples, client assessment summary reports 501 include effectiveness measures 503, component assessment measures 505, and resonance measures 507. Effectiveness assessment measures include composite assessment measure(s), industry/category/client specific placement (percentile, ranking, etc.), actionable grouping assessment such as removing material, modifying segments, or fine tuning specific elements, etc., and the evolution of the effectiveness profile over time. In particular examples, component assessment reports include component assessment measures like attention, emotional engagement scores, percentile placement, ranking, etc. Component profile measures include time based evolution of the component measures and profile statistical assessments. According to various examples, reports include the number of times material is assessed, attributes of the multiple presentations used, evolution of the response assessment measures over the multiple presentations, and usage recommendations.

According to various examples, client cumulative reports 511 include media grouped reporting 513 of all media assessed, campaign grouped reporting 515 of media assessed, and time/location grouped reporting 517 of media assessed. According to various examples, industry cumulative and syndicated reports 521 include aggregate assessment responses measures 523, top performer lists 525, bottom performer lists 527, outliers 529, and trend reporting 531. In particular examples, tracking and reporting includes specific products, categories, companies, brands. According to various examples, prediction reports 533 are also generated. Prediction reports may include brand affinity prediction 535, product pathway prediction 537, purchase intent prediction 539, and ratings prediction 541.

FIG. 6 illustrates one example of ratings prediction. At 601, media material is provided to multiple subjects. According to various examples, media includes streaming video and audio. In particular examples, subjects view media in their own homes in group or individual settings. In some examples, verbal and written responses are collected for use without neuro-response measurements. In other examples, verbal and written responses are correlated with neuro-response measurements. At 603, subject neuro-response measurements are collected using a variety of modalities, such as EEG, ERP, EOG, GSR, etc. At 605, data is passed through a data cleanser to remove noise and artifacts that may make data more difficult to interpret. According to various examples, the data cleanser removes EEG electrical activity associated with blinking and other endogenous/exogenous artifacts.

According to various examples, data analysis is performed. Data analysis may include intra-modality response synthesis and cross-modality response synthesis to enhance effectiveness measures. It should be noted that in some particular instances, one type of synthesis may be performed without performing other types of synthesis. For example, cross-modality response synthesis may be performed with or without intra-modality synthesis.

A variety of mechanisms can be used to perform data analysis. In particular examples, a media attributes repository is accessed to obtain attributes and characteristics of the media materials, along with purposes, intents, objectives, etc. In particular examples, EEG response data is synthesized to provide an enhanced assessment of effectiveness. According to various examples, EEG measures electrical activity resulting from thousands of simultaneous neural processes associated with different portions of the brain. EEG data can be classified in various bands. According to various examples, brainwave frequencies include delta, theta, alpha, beta, and gamma frequency ranges. Delta waves are classified as those less than 4 Hz and are prominent during deep sleep. Theta waves have frequencies between 3.5 to 7.5 Hz and are associated with memories, attention, emotions, and sensations. Theta waves are typically prominent during states of internal focus.

Alpha frequencies reside between 7.5 and 13 Hz and typically peak around 10 Hz. Alpha waves are prominent during states of relaxation. Beta waves have a frequency range between 14 and 30 Hz. Beta waves are prominent during states of motor control, long range synchronization between brain areas, analytical problem solving, judgment, and decision making. Gamma waves occur between 30 and 60 Hz and are involved in binding of different populations of neurons together into a network for the purpose of carrying out a certain cognitive or motor function, as well as in attention and memory. Because the skull and dermal layers attenuate waves in this frequency range, brain waves above 75-80 Hz are difficult to detect and are often not used for stimuli response assessment.

However, the techniques and mechanisms of the present disclosure recognize that analyzing high gamma band (kappa-band: Above 60 Hz) measurements, in addition to theta, alpha, beta, and low gamma band measurements, enhances neurological attention, emotional engagement and retention component estimates. In particular examples, EEG measurements including difficult to detect high gamma or kappa band measurements are obtained, enhanced, and evaluated. Subject and task specific signature sub-bands in the theta, alpha, beta, gamma and kappa bands are identified to provide enhanced response estimates. According to various examples, high gamma waves (kappa-band) above 80 Hz (typically detectable with sub-cranial EEG and/or magnetoencephalography) can be used in inverse model-based enhancement of the frequency responses to the stimuli.

Various examples of the present disclosure recognize that particular sub-bands within each frequency range have particular prominence during certain activities. A subset of the frequencies in a particular band is referred to herein as a sub-band. For example, a sub-band may include the 40-45 Hz range within the gamma band. In particular examples, multiple sub-bands within the different bands are selected while remaining frequencies are band pass filtered. In particular examples, multiple sub-band responses may be enhanced, while the remaining frequency responses may be attenuated.

An information theory based band-weighting model is used for adaptive extraction of selective dataset specific, subject specific, task specific bands to enhance the effectiveness measure. Adaptive extraction may be performed using fuzzy scaling. Stimuli can be presented and enhanced measurements determined multiple times to determine the variation profiles across multiple presentations. Determining various profiles provides an enhanced assessment of the primary responses as well as the longevity (wear-out) of the marketing and entertainment stimuli. The synchronous response of multiple individuals to stimuli presented in concert is measured to determine an enhanced across subject synchrony measure of effectiveness. According to various examples, the synchronous response may be determined for multiple subjects residing in separate locations or for multiple subjects residing in the same location.

Although a variety of synthesis mechanisms are described, it should be recognized that any number of mechanisms can be applied—in sequence or in parallel with or without interaction between the mechanisms.

Although intra-modality synthesis mechanisms provide enhanced significance data, additional cross-modality synthesis mechanisms can also be applied. A variety of mechanisms such as EEG, Eye Tracking, GSR, EOG, and facial emotion encoding are connected to a cross-modality synthesis mechanism. Other mechanisms as well as variations and enhancements on existing mechanisms may also be included. According to various examples, data from a specific modality can be enhanced using data from one or more other modalities. In particular examples, EEG typically makes frequency measurements in different bands like alpha, beta and gamma to provide estimates of significance. However, the techniques of the present disclosure recognize that significance measures can be enhanced further using information from other modalities.

For example, facial emotion encoding measures can be used to enhance the valence of the EEG emotional engagement measure. EOG and eye tracking saccadic measures of object entities can be used to enhance the EEG estimates of significance including but not limited to attention, emotional engagement, and memory retention. According to various examples, a cross-modality synthesis mechanism performs time and phase shifting of data to allow data from different modalities to align. In some examples, it is recognized that an EEG response will often occur hundreds of milliseconds before a facial emotion measurement changes. Correlations can be drawn and time and phase shifts made on an individual as well as a group basis. In other examples, saccadic eye movements may be determined as occurring before and after particular EEG responses. According to various examples, time corrected GSR measures are used to scale and enhance the EEG estimates of significance including attention, emotional engagement and memory retention measures.

Evidence of the occurrence or non-occurrence of specific time domain difference event-related potential components (like the DERP) in specific regions correlates with subject responsiveness to specific media. According to various examples, ERP measures are enhanced using EEG time-frequency measures (ERPSP) in response to the presentation of the marketing and entertainment stimuli. Specific portions are extracted and isolated to identify ERP, DERP and ERPSP analyses to perform. In particular examples, an EEG frequency estimation of attention, emotion and memory retention (ERPSP) is used as a co-factor in enhancing the ERP, DERP and time-domain response analysis.

EOG measures saccades to determine the presence of attention to specific objects of media. Eye tracking measures the subject's gaze path, location and dwell on specific objects of media. According to various examples, EOG and eye tracking is enhanced by measuring the presence of lambda waves (a neurophysiological index of saccade effectiveness) in the ongoing EEG in the occipital and extra striate regions, triggered by the slope of saccade-onset to estimate the significance of the EOG and eye tracking measures. In particular examples, specific EEG signatures of activity such as slow potential shifts and measures of coherence in time-frequency responses at the Frontal Eye Field (FEF) regions that preceded saccade-onset are measured to enhance the effectiveness of the saccadic activity data.

GSR typically measures the change in general arousal in response to media presented. According to various examples, GSR is enhanced by correlating EEG/ERP responses and the GSR measurement to get an enhanced estimate of subject engagement. The GSR latency baselines are used in constructing a time-corrected GSR response to the media. The time-corrected GSR response is co-factored with the EEG measures to enhance GSR significance measures.

According to various examples, facial emotion encoding uses templates generated by measuring facial muscle positions and movements of individuals expressing various emotions prior to the testing session. These individual specific facial emotion encoding templates are matched with the individual responses to identify subject emotional response. In particular examples, these facial emotion encoding measurements are enhanced by evaluating inter-hemispherical asymmetries in EEG responses in specific frequency bands and measuring frequency band interactions. The techniques of the present disclosure recognize that not only are particular frequency bands significant in EEG responses, but particular frequency bands used for communication between particular areas of the brain are significant. Consequently, these EEG responses enhance the EMG, graphic and video based facial emotion identification.

According to various examples, post-stimulus versus pre-stimulus differential measurements of ERP time domain components in multiple regions of the brain (DERP) are measured at multiple regions of the brain at 607. The differential measures give a mechanism for eliciting responses attributable to the stimulus. For example, the messaging response attributable to an advertisement or the brand response attributable to multiple brands is determined using pre-resonance and post-resonance estimates.

At 609, target versus distracter stimulus differential responses are determined for different regions of the brain (DERP). At 611, event related time-frequency analysis of the differential response (DERPSPs) are used to assess the attention, emotion and memory retention measures across multiple frequency bands. According to various examples, the multiple frequency bands include theta, alpha, beta, gamma and high gamma or kappa.

At 613, actual ratings information for analyzed media materials are determined and integrated. According to various examples, actual resulting ratings along with demographic data is integrated with neuro-response data for large number of subjects in various geographic and demographic groups and subgroups. At 617, integrated data is sent to a ratings prediction generator repository. The ratings prediction generator repository may be used to predict behavior resulting from exposure to new media materials using information about a user and resulting neuro-response data.

FIG. 7 illustrates an example of a technique for ratings prediction. At 701, characteristics of source material are determined. According to various examples, source material itself includes metatags associated with various spatial and temporal locations indicating the level of priming for various products, services, and offerings. The characteristics may be obtained from a personalization repository system or may be obtained dynamically from a data analyzer. At 703, neuro-response data is obtained for multiple users using multiple modalities. At 705, survey and resulting behavior information is integrated from the ratings prediction generator repository.

According to various examples, media materials are categorized and other media materials having similar tags and characteristics are identified at 707. At 709, ratings are predicted for the media materials. According to various examples, ratings are predicted by determining ratings for corresponding media materials that have similar neuro-response characteristics for various demographic groups. In particular examples, similar neuro-response patterns to similar media materials are referenced to determine prior elicited expressions.

According to various examples, various mechanisms such as the data collection mechanisms, the intra-modality synthesis mechanisms, cross-modality synthesis mechanisms, etc. are implemented on multiple devices. However, it is also possible that the various mechanisms be implemented in hardware, firmware, and/or software in a single system. FIG. 8 provides one example of a system that can be used to implement one or more mechanisms. For example, the system shown in FIG. 8 may be used to implement a resonance measurement system.

According to particular examples, a system 800 suitable for implementing particular examples of the present disclosure includes a processor 801, a memory 803, an interface 811, and a bus 815 (e.g., a PCI bus). When acting under the control of appropriate software or firmware, the processor 801 is responsible for such tasks such as pattern generation. Various specially configured devices can also be used in place of a processor 801 or in addition to processor 801. The complete implementation can also be done in custom hardware. The interface 811 is typically configured to send and receive data packets or data segments over a network. Particular examples of interfaces the device supports include host bus adapter (HBA) interfaces, Ethernet interfaces, frame relay interfaces, cable interfaces, DSL interfaces, token ring interfaces, and the like.

According to particular examples, the system 800 uses memory 803 to store data, algorithms and program instructions. The program instructions may control the operation of an operating system and/or one or more applications, for example. The memory or memories may also be configured to store received data and process received data.

Because such information and program instructions may be employed to implement the systems/methods described herein, the present disclosure relates to tangible, machine readable media that include program instructions, state information, etc. for performing various operations described herein. Examples of machine-readable media include, but are not limited to, magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROM disks and DVDs; magneto-optical media such as optical disks; and hardware devices that are specially configured to store and perform program instructions, such as read-only memory devices (ROM) and random access memory (RAM). Examples of program instructions include both machine code, such as produced by a compiler, and files containing higher level code that may be executed by the computer using an interpreter.

Although the foregoing disclosure has been described in some detail for purposes of clarity of understanding, it will be apparent that certain changes and modifications may be practiced within the scope of the appended claims. Therefore, the present examples are to be considered as illustrative and not restrictive and the disclosure is not to be limited to the details given herein, but may be modified within the scope and equivalents of the appended claims. 

1.-20. (canceled)
 21. A system for transforming neuro-response data into media ratings, the system comprising: a data collector to obtain first neuro-response from a first subject exposed to a first media and second neuro-response data from a second subject exposed to a second media, the first media broadcast before a time of the second media; and an analyzer to: integrate the first neuro-response data with ratings data for the first media to generate a first rating for the first media, the ratings data based on set-top box data associated with a media presentation device presenting the first media; and transform the second neuro-response data into a second rating for the second media based on the first rating.
 22. The system of claim 21, wherein the analyzer is to identify third media based on the second rating and output the third media for exposure to a subject.
 23. The system of claim 21, wherein the analyzer is to identify the third media based on a metatag associated with the third media and at least one of a meta-tag associated with the first media or a meta-tag associated with the second media.
 24. The system of claim 21, wherein the analyzer is to modify a portion of the second media based on the second rating and output the modified second media for exposure to a subject.
 25. The system of claim 21, wherein the analyzer is to tag the first media with first metadata identifying a first attribute of the first media and tag the second media with second metadata identifying a second attribute of the second media, the first metadata and the second metadata being substantially similar.
 26. The system of claim 21, wherein the first neuro-response data exhibits a first neuro-response pattern and the second neuro-response data exhibits a second neuro-response pattern, the first neuro-response pattern and the second neuro-response pattern being similar.
 27. The system of claim 21, wherein the data collector is time-synchronized with a set-top box that is to generate the set-top box data, the analyzer to integrate the first neuro-response data with the ratings data for the first media based on the synchronization between the data collector and the set-top box.
 28. The system of claim 21, wherein the analyzer is to further integrate demographic data with the first neuro-response data and the ratings data.
 29. A tangible machine readable storage device or storage disc comprising instructions which, when executed by a machine, cause the machine to at least: obtain first neuro-response from a first subject exposed to a first media and second neuro-response data from a second subject exposed to a second media, the first media broadcast before a time of the second media; integrate the first neuro-response data with ratings data for the first media to generate a first rating for the first media, the ratings data based on set-top box data associated with a media presentation device presenting the first media; and transform the second neuro-response data into a second rating for the second media based on the first rating.
 30. The storage device or storage disk of claim 29, wherein the instructions, when executed, further cause the machine to identify third media based on the second rating and output the third media for exposure to a subject.
 31. The storage device or storage disk of claim 29, wherein the instructions, when executed, further cause the machine to identify the third media based on a meta-tag associated with the third media and at least one of a meta-tag associated with the first media or a metatag associate with the second media.
 32. The storage device or storage disk of claim 29, wherein the instructions, when executed, further cause the machine to modify a portion of the second media based on the second rating and output the modified second media for exposure to a subject.
 33. The storage device or storage disk of claim 29, wherein the instructions, when executed, further cause the machine to tag the first media with first metadata identifying a first attribute of the first media and tag the second media with second metadata identifying a second attribute of the second media, the first metadata and the second metadata being substantially similar.
 34. The storage device or storage disk of claim 29, wherein the first neuro-response data exhibits a first neuro-response pattern and the second neuro-response data exhibits a second neuro-response pattern, the first neuro-response pattern and the second neuro-response pattern being similar.
 35. A system for transforming neuro-response data into media ratings, the system comprising: a data collector to obtain first neuro-response data from a first subject exposed to first media; and an analyzer to: retrieve a first meta-tag representative of an attribute of the first media; identify second media based on the first meta-tag and a second meta-tag representative of an attribute of the second media; identify second neuro-response data associated with the second media, the second neuro-response data collected from a second subject exposed to the second media, the second media having been broadcast before a time of broadcast of the first media; identify a first rating associated with the second media; and transform the first neuro-response data into a second rating for the first media based on the first rating.
 36. The system of claim 35, wherein the analyzer is to further identify the second neuro-response data based on a threshold of similarity between a first neuro-response pattern exhibited by the first neuro-response data and a second neuro-response pattern exhibited by the second neuro-response data.
 37. The system of claim 36, wherein the first neuro-response pattern is representative of an interaction between a first frequency band of electroencephalographic data and a second frequency band of the electroencephalographic data.
 38. The system of claim 37, wherein the interaction includes a degree of coherence between a first change in amplitude of the first frequency band measured before a neurological event and a second change in amplitude of the second frequency band measured before the neurological event.
 39. The system of claim 35, wherein the first neuro-response data and the second neuro-response data are each collected from a same region of respective brains of the first subject and the second subject.
 40. The system of claim 35, wherein the first subject and the second subject are associated with similar demographic groups. 